The TV experience can be improved with new interactive features taking into account the individuality of TV viewers: content recommendation, Electronic Program Guide with preferred channels, personal widget space, parental control.
In this context, it becomes important to know who is actually watching TV. To answer this question, it is foreseen to use biometric sensors to recognize householders who have originally been enrolled in the system. The benefit is twofold: some biometric technologies ensure effortless/covert identification; in addition, they usually offer a means to reliably authenticate users.
Face recognition is a very promising technology. However, a camera mounted on top of TV embraces a wide area encompassing multiple people. If a face recognition technology for video surveillance is run, several problems have to be solved:                The face recognition algorithm is computationally intensive and the CPU load is proportional to the number of people in the scene. For video-surveillance applications, the algorithm is usually run on a high-end PC whereas the TV viewer identification is supposed to run on limited CPU. If our goal is to identify all the TV viewers to provide a personalized experience for the group, the problem we have to solve is to simplify the algorithm taking into account the specifics of the scene in order to ease the integration in a CE device.        Though, it looks quite difficult to provide a personalized TV experience for a group of people since it is tough to infer a group's profile from multiple individual profiles. In addition, not all the people in the scope of the camera are interested in the TV experience. Therefore, an alternative solution is to identify a unique TV viewer who will act as a primary viewer in order to subsequently personalize the TV experience for this person only. We will assume that this primary viewer is the one who holds the remote control. The problem we have to solve is to recognize the face of the person holding the remote control.        